Physiological Signal Data Logger

Data is the key to any scientific research. Today, many data-recording devices for obtaining raw data are available in the market. Versatile Innovations is developing such a data-recording device that is portable, lightweight and capable of capturing and recording 16 different channels of physiological signals simultaneously. This device would help researchers at the Living Laboratory to conduct research activities that improve the “fit” between people and their daily living and working environments by studying people’s interaction with devices, assistive technology, and environmental features. Our 16-channel physiological signal data logger allows monitoring and recording of electrical activities of the heart, muscle and brain, the arterial oxygen saturation level in the blood stream, and body temperature. It also records data collected from force, acceleration, and pressure sensors to monitor physical activities

Higher Levels of Early Childhood Caries (ECC) Is Associated with Developing Psychomotor Deficiency: The Cross- Sectional Bi-Township Analysis for The New Hypothesis

The aim of this study was to reassess and confirm the relationship between early childhood caries (ECC) and manifestations of psychomotor deficiency in 4–6-yr-old kindergarteners, which has remained elusive to date. A cross-sectional study with bi-township analysis was designed whereby 353 kindergarteners, aged 4–6 whose caries were greater (dmft (decayed, missing and filled teeth, dmft index) = 5.25) than that of the national average, located in a rural township of central Taiwan were recruited using simple random-selection. Besides the personal, demographic, and dietary information, the measurements for caries and the amended comprehensive scales (CCDI) of children’s psychomotor development were used to address their relationship. One-way ANOVA vs. multiple linear regression were employed to compare the differences of variables between age, gender, BMI (Body Mass Index), and dmft scores vs. relationships among all variables, respectively. The results confirmed that there was a positive relationship between severe ECC (dmft > 3~8) and psychomotor deficiency (i.e., expressive language and comprehension-concept scales, etc.) amongst the kindergarteners analyzed. Our cross-sectional bi-township analysis has confirmed that there is indeed an association between severe ECC and psychomotor deficiency in kindergarteners, and we suggest that this may arise through critical stages of growth, not only via personal language communications, but psycho-social engagements as well. Therefore, a new hypothesis is proposed

A real-world clinical validation for AI-based MRI monitoring in multiple sclerosis.

Acknowledgements: The authors would like to acknowledge funding support from Australian Government Department of Industry (CRCPFIVE000141). C.W. would like to acknowledge the support from the Nerve Research Foundation at The University of Sydney and Multiple Sclerosis Australia (18–0461).Modern management of MS targets No Evidence of Disease Activity (NEDA): no clinical relapses, no magnetic resonance imaging (MRI) disease activity and no disability worsening. While MRI is the principal tool available to neurologists for monitoring clinically silent MS disease activity and, where appropriate, escalating treatment, standard radiology reports are qualitative and may be insensitive to the development of new or enlarging lesions. Existing quantitative neuroimaging tools lack adequate clinical validation. In 397 multi-center MRI scan pairs acquired in routine practice, we demonstrate superior case-level sensitivity of a clinically integrated AI-based tool over standard radiology reports (93.3% vs 58.3%), relative to a consensus ground truth, with minimal loss of specificity. We also demonstrate equivalence of the AI-tool with a core clinical trial imaging lab for lesion activity and quantitative brain volumetric measures, including percentage brain volume loss (PBVC), an accepted biomarker of neurodegeneration in MS (mean PBVC -0.32% vs -0.36%, respectively), whereas even severe atrophy (>0.8% loss) was not appreciated in radiology reports. Finally, the AI-tool additionally embeds a clinically meaningful, experiential comparator that returns a relevant MS patient centile for lesion burden, revealing, in our cohort, inconsistencies in qualitative descriptors used in radiology reports. AI-based image quantitation enhances the accuracy of, and value-adds to, qualitative radiology reporting. Scaled deployment of these tools will open a path to precision management for patients with MS

A real-world clinical validation for AI-based MRI monitoring in multiple sclerosis.

Acknowledgements: The authors would like to acknowledge funding support from Australian Government Department of Industry (CRCPFIVE000141). C.W. would like to acknowledge the support from the Nerve Research Foundation at The University of Sydney and Multiple Sclerosis Australia (18–0461).Modern management of MS targets No Evidence of Disease Activity (NEDA): no clinical relapses, no magnetic resonance imaging (MRI) disease activity and no disability worsening. While MRI is the principal tool available to neurologists for monitoring clinically silent MS disease activity and, where appropriate, escalating treatment, standard radiology reports are qualitative and may be insensitive to the development of new or enlarging lesions. Existing quantitative neuroimaging tools lack adequate clinical validation. In 397 multi-center MRI scan pairs acquired in routine practice, we demonstrate superior case-level sensitivity of a clinically integrated AI-based tool over standard radiology reports (93.3% vs 58.3%), relative to a consensus ground truth, with minimal loss of specificity. We also demonstrate equivalence of the AI-tool with a core clinical trial imaging lab for lesion activity and quantitative brain volumetric measures, including percentage brain volume loss (PBVC), an accepted biomarker of neurodegeneration in MS (mean PBVC -0.32% vs -0.36%, respectively), whereas even severe atrophy (>0.8% loss) was not appreciated in radiology reports. Finally, the AI-tool additionally embeds a clinically meaningful, experiential comparator that returns a relevant MS patient centile for lesion burden, revealing, in our cohort, inconsistencies in qualitative descriptors used in radiology reports. AI-based image quantitation enhances the accuracy of, and value-adds to, qualitative radiology reporting. Scaled deployment of these tools will open a path to precision management for patients with MS